{"title":"“定制化”创新通过一个相互适应和学习的环境的出现","authors":"S. Harkema, W. Baets","doi":"10.1051/EJESS:2001111","DOIUrl":null,"url":null,"abstract":"– This paper describes an experimental approach to model an innovation process on the basis of principles and concepts of complexity theory and its possible implications on the outcome of the innovation process. It is part of ongoing research carried out at the University of Nyenrode in the Netherlands, at the Nyenrode Institute of Virtual Education and Knowledge Management. In this paper the intricate and complex relation underlying the process of new product development and customer response, is the focus of attention. This relation is primarily defined as a process of knowledge management and mutual learning (Baets, 1998). In addition innovation is defined as a process of “manageable chaos” (adapted from Quinn, 1985). This means that innovation is conceptualized as a process of interaction and subsequent knowledge flows between people that are organized in a network and form a complex system. In the sixties Simon, one of the founders of complexity theory, defined a complex system as one made up of many parts that have many intricate interactions. An alternative will be brought forward to model innovation processes. Instead of defining innovation success in terms of organizational characteristics or factors linked to the success rate of a product innovation; the latter will be modeled as the outcome of interaction among a variety of agents that pursue strategies in a co-evolutionary process with each other. Classification Codes: M21.","PeriodicalId":352454,"journal":{"name":"European Journal of Economic and Social Systems","volume":"5 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"“Customerized\\\" innovation through the emergence of a mutually adaptive and learning environment\",\"authors\":\"S. Harkema, W. Baets\",\"doi\":\"10.1051/EJESS:2001111\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"– This paper describes an experimental approach to model an innovation process on the basis of principles and concepts of complexity theory and its possible implications on the outcome of the innovation process. It is part of ongoing research carried out at the University of Nyenrode in the Netherlands, at the Nyenrode Institute of Virtual Education and Knowledge Management. In this paper the intricate and complex relation underlying the process of new product development and customer response, is the focus of attention. This relation is primarily defined as a process of knowledge management and mutual learning (Baets, 1998). In addition innovation is defined as a process of “manageable chaos” (adapted from Quinn, 1985). This means that innovation is conceptualized as a process of interaction and subsequent knowledge flows between people that are organized in a network and form a complex system. In the sixties Simon, one of the founders of complexity theory, defined a complex system as one made up of many parts that have many intricate interactions. An alternative will be brought forward to model innovation processes. Instead of defining innovation success in terms of organizational characteristics or factors linked to the success rate of a product innovation; the latter will be modeled as the outcome of interaction among a variety of agents that pursue strategies in a co-evolutionary process with each other. Classification Codes: M21.\",\"PeriodicalId\":352454,\"journal\":{\"name\":\"European Journal of Economic and Social Systems\",\"volume\":\"5 4\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Economic and Social Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1051/EJESS:2001111\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Economic and Social Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1051/EJESS:2001111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
“Customerized" innovation through the emergence of a mutually adaptive and learning environment
– This paper describes an experimental approach to model an innovation process on the basis of principles and concepts of complexity theory and its possible implications on the outcome of the innovation process. It is part of ongoing research carried out at the University of Nyenrode in the Netherlands, at the Nyenrode Institute of Virtual Education and Knowledge Management. In this paper the intricate and complex relation underlying the process of new product development and customer response, is the focus of attention. This relation is primarily defined as a process of knowledge management and mutual learning (Baets, 1998). In addition innovation is defined as a process of “manageable chaos” (adapted from Quinn, 1985). This means that innovation is conceptualized as a process of interaction and subsequent knowledge flows between people that are organized in a network and form a complex system. In the sixties Simon, one of the founders of complexity theory, defined a complex system as one made up of many parts that have many intricate interactions. An alternative will be brought forward to model innovation processes. Instead of defining innovation success in terms of organizational characteristics or factors linked to the success rate of a product innovation; the latter will be modeled as the outcome of interaction among a variety of agents that pursue strategies in a co-evolutionary process with each other. Classification Codes: M21.